Evaluating Machine Learning Algorithms for Predictive Modeling of Large-scale Event Attendance

Abstract

Predicting attendance at large-scale public events is a critical task to support better resource planning, logistics, and safety management. This study investigates the performance of various machine learning models in forecasting event attendance using metadata features such as event type, venue, location, date, and duration. The dataset comprises over 19526 event records obtained from a U.S. government open data repository, covering multiple years and diverse event categories. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). Among the models tested, ensemble methods particularly Gradient Boosting Regressor and XGBoost outperformed others, achieving the lowest MAE (61.37 and 59.52, respectively) and the highest R² values (0.22 and 0.15). These results suggest superior generalization capability in capturing complex nonlinear patterns in the data. In contrast, linear models and simpler non-parametric methods such as Decision Trees and K-Nearest Neighbors (KNN) exhibited relatively weaker predictive accuracy, with R² scores close to or below 0.14. While the R² values indicate that metadata alone provides a limited view of attendance dynamics, the relatively low MAE across models implies that reasonable point predictions are still achievable. These findings highlight the potential of ensemble-based methods for baseline forecasting tasks. Furthermore, the study underscores the importance of incorporating richer feature sets such as pricing, weather, promotional activity, and social sentiment for future model improvement. This research provides a foundational benchmark for data-driven attendance forecasting and offers practical implications for event organizers seeking scalable, automated prediction tools to support strategic planning.